Research Of Fault Intelligent Diagnosis Based On Bayesian Network

In the field of fault diagnosis, uncertainty problems are in the majority, mainly caused by the complexity of the target diagnosis, the limitations of the testing means, and imprecise knowledge. Especially for large and complex electrical equipment such as turbo-generator unit, there are a number of complex and coupling relationships among its components as well as themselves internal. Because of uncertain factors the faults may be multiple failures, associated faults or other complex forms. Therefore, how to resolve the uncertainty of turbo-generator fault diagnosis becomes the most important issue.Common methods of settling uncertainties problem include the Bayesian method, rough set theory, proof theory. Through Agre G and other expert research and analysis, Bayesian network based on Bayesian theory is the most effective method to solve the uncertainty problem.This paper studies the latest developments in Bayesian network express, learning and reasoning. The Bayesian network simplified inference algorithm is proposed after researching Bayesian network inference deeply. In order to overcome the memory occupied problem, a new optimizing algorithm based on depth-first branch and bound is proposed. It costs a little time for great memory space, solves the memory allocation problem. The method has a strong practical value.Take uncertainty problem of turbo-generator unit fault diagnosis as the research background, we systematically review the rotating machinery fault diagnosis methods and principles, expound the common turbo-generator abnormal vibration and analyze vibration signals in frequency domain characteristics. To solve the problem of uncertainty the fault diagnosis network model is proposed, and model of knowledge representation, method of construction is researched deeply.The turbo-generator fault diagnosis method based on the principal component analysis and Bayesian network is proposed. We using principal component analysis method to extract the easy handling fault symptoms and obtain the initial fault mode inclination, then the fault mode inclination will be fault nodes of Bayesian network, and further diagnostic analysis. The method avoids the principal component analysis indicator system identified difficult problem, and Bayesian network is better to integrate the various symptoms to diagnose, thereby enhancing the reliability of the diagnosis results. Based on complementary intelligent blending thought we combine rough set theory and Bayesian network organically, propose a new turbo-generator fault diagnosis method. Using knowledge reduced technology of rough set theory to compress expertise knowledge, remove redundant information, then obtain the minimum diagnosis rules. At the same time use of Bayesian network to discover the potential relationship among nodes, then build the turbo-generator fault diagnosis Bayesian network model. Further more it can raise diagnosis efficiency.